Antenatal care utilization and its Associated factors in Nigeria: Poisson-Compound models approach
DOI:
https://doi.org/10.30442/Keywords:
Antenatal care, Count regression, Maternal health, Nigeria, Zero-inflated modelAbstract
Background: Antenatal care (ANC) provides preventive, promotive, and curative services during pregnancy and contributes to improved maternal and neonatal outcomes. Despite its benefits, ANC utilization remains suboptimal in Nigeria. Due to this public health importance, there is a need to identify factors associated with ANC utilization using appropriate statistical model.
Objective: This study compared Poisson compound models to determine the most suitable model for antenatal care visits and its associated factors in Nigeria.
Methods: This study used secondary data from the 2024 Nigeria Demographic and Health Survey (NDHS). The outcome variable was the number of antenatal care visits. Four count regression models were fitted: Poisson Regression, Zero-Inflated Poisson (ZIP), Negative Binomial (NB), and Zero-Inflated Negative Binomial (ZINB) models. Model comparison was based on Deviance (Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Incidence Rate Ratios (IRR). P-value <0.05 was considered statistically significant.
Results: The Zero-Inflated Negative Binomial model produced the lowest Deviance (61964.5), AIC (62030.5), and BIC (62278.7) values compared with the other models, indicating the best fit for antenatal care utilization. ANC utilization was associated with maternal age, geopolitical zone, residence, education, religion, marital status, employment, birth order, partner’s education/occupation, wealth, and healthcare distance (IRR=0.843–1.788; p<0.05).
Conclusion: The ZINB model was the most appropriate model for antenatal care visits in Nigeria. The findings also emphasize the need for robust analytical approaches in evaluating maternal healthcare utilization patterns in developing countries with limited health resources.
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